A Novel adaptive genetic algorithm for Dynamic Vehicle Routing Problem with Backhaul and Two-dimensional loading constraints, A Case in Tunisian Posta

In this paper, we consider an extension of the Dynamic Vehicle Routing Problem with Backhauls integrated with two-dimensional loading problem called DVRPB with 2D loading constraints (2L-DVRPB). In the VRPB, a vehicle can deliver (Linehaul) then collect goods from customers (backhaul) and bring back to the depot. Once customer demand is formed by a set of two-dimensional items the problem will be treat as a 2L-VRPB. The 2L-VRPB has been studied on the static case. However, in most real-life application, new customer requests can be happen over time of backhaul and thus perturb the optimal routing schedule that was originally invented. This problem has not been analysed sofar in the literature. The 2L-DVRPB is an NP-Hard problem, so, we propose to use a Genetic algorithm for routing and a packing problems. We applied our approach in a real case study of the Regional Post Office of the city of Jendouba in the North of Tunisia. Results indicate that the AGA approach is considered as the best approach in terms of solutions quality for a real world routing system.

A Hybrid Metaheuristic Algorithm for the Green Vehicle Routing Problem in the Dynamic Environment

The main goal of this research is to combine the dynamic vehicle routing problem (DVRP), in which new information is received along the day, and the green vehicle routing problem (GVRP), which determines the routes of vehicles to serve a set of customers, while minimizing the total amount of the greenhouse gas. In this study, the customers must be serviced at their locations by a set of vehicles in real time by minimizing the carbon dioxide CO2. For that, the authors present the technique employed to estimate the amount of CO2 emissions and integrate it into the proposed mathematic model. Then they develop the resolution approach based on ant colony optimization (ACO) algorithm and a large neighborhood search (LNS) algorithm. The effectiveness of this approach is tested on a set of the static and dynamic green problems instances.

A Multicircle Order Acceptance Strategy for Dynamic Vehicle Routing Problems Considering Customer Choice of Last-Mile Delivery Modes and Time Slots

A multicircle order acceptance strategy was proposed to decide whether to accept customer requests for specific last-mile delivery modes (including attended home or reception box delivery) and time slots. The strategy was composed of initializing acceptable time slot allocations, reallocating acceptable time slots, matching reference sites, and assessing time slot deviations. A strategy-oriented insertion algorithm for dynamic vehicle routing problems with hard time windows was constructed, thereby showing the proposed strategy achieves a better balance between revenue and distance than the “first come and first served” strategy. Always accepting global optimization result is not significantly better than adopting the result conditionally or based on simulated annealing theory. The distance and revenue gradually increase with the number of reference sites, while revenue/distance ratio decreases. The vehicles are available to serve more attended home delivery orders with a gradual increase in time slot interval, thereby leading to an increase of AHD and total revenue.

Current State of Dynamic Vehicle Routing Problems Solved by Ant Colony Optimization Algorithm

Dynamic Vehicle Routing Problem is a more complex version of Vehicle Routing Problem, closer to the present, real-world problems. Heuristic methods are used to solve the problem as Vehicle Routing Problem is NP-hard. Among many different solution methods, the Ant Colony Optimization algorithm is proven to be the efficient solution when dealing with the dynamic version of the problem. Even though this problem is known to the scientific community for decades, the field is extremely active due to technological advancements and the current relevance of the problem. As various sub-types of routing problems and solution methods exist, there is a great number of possible problem-solution combinations and research directions. This paper aims to make a focused review of the current state in the field of Dynamic Vehicle Routing Problems solved by Ant Colony Optimization algorithm, to establish current trends in the field.

Dynamic Vehicle Routing with Parking Probability under Connected Environment

In downtown areas of large cities, it is very challenging for drivers to find available parking spots, even when they are provided with information on parking availability and location information. To overcome this challenge, this paper develops a dynamic vehicle routing system to search for the optimal routes for connected vehicles to find parking spots successfully and to minimize total trip time, including driving time and walking time. The system predicts the probability of each parking lot having available parking spots based on the existing available number of spots and the vehicle arrival and departure rates collected by connected vehicles. This probability is integrated in the search for vehicle routes to minimize total travel and walking times. Numerical experiments indicate that the proposed system can reduce the cruising time spent searching for available parking spots, and the total trip time can be reduced by up to 24%. In addition, the system can decrease the number of re-routing decisions, which reduces the stress of drivers on the road. A sensitivity analysis of the parking probability is also conducted. Some future work based on the proposed system is proposed in the conclusion to this paper.